{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "amended-composer",
   "metadata": {},
   "source": [
    "框架\n",
    "\n",
    "    一、导入数据\n",
    "    二、原始序列的检验\n",
    "    三、一阶差分序列的检验\n",
    "    四、定阶（参数调优)\n",
    "    五、建模与预测\n",
    "    \n",
    "    参考材料：《Python数据分析与挖掘实践》"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "quarterly-measurement",
   "metadata": {},
   "source": [
    "## 工具准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "short-partner",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import json\n",
    "import itertools\n",
    "\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "from matplotlib.pylab import style #自定义图表风格\n",
    "style.use('ggplot')\n",
    "\n",
    "# 解决中文乱码问题\n",
    "plt.rcParams['font.sans-serif'] = ['Simhei']\n",
    "# 解决坐标轴刻度负号乱码\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "#pip install statsmodels\n",
    "from statsmodels.graphics.tsaplots import plot_acf,plot_pacf  #自相关图、偏自相关图\n",
    "from statsmodels.tsa.stattools import adfuller as ADF #平稳性检验\n",
    "from statsmodels.stats.diagnostic import acorr_ljungbox #白噪声检验\n",
    "import statsmodels.api as sm #D-W检验,一阶自相关检验\n",
    "from statsmodels.graphics.api import qqplot #画QQ图,检验一组数据是否服从正态分布\n",
    "from statsmodels.tsa.arima.model import ARIMA"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "chicken-metabolism",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "liquid-listening",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('../saleByTopFromSeries.json','r',encoding='utf-8') as f:\n",
    "    listRS = json.load(f)\n",
    "index = 6\n",
    "pyearAndMonth = listRS[index][\"yearAndMonth\"]\n",
    "yearAndMonth = pyearAndMonth[:42]\n",
    "psalesData= listRS[index][\"salesData\"]\n",
    "salesData = psalesData[:42]\n",
    "series = listRS[index][\"series\"]\n",
    "seriesId = listRS[index][\"seriesId\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "mounted-bearing",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建字典对象\n",
    "pdata = {\"date\":pyearAndMonth,\"sale\":psalesData}\n",
    "\n",
    "# 创建dataframe\n",
    "psale = pd.DataFrame(pdata)\n",
    "\n",
    "# # 转换日期数据类型\n",
    "# index = pd.DatetimeIndex(yearAndMonth)\n",
    "\n",
    "# 将日期设置索引值\n",
    "psale.set_index(['date'],inplace=True)\n",
    "\n",
    "# 转换销售量数据类型\n",
    "psale.sale=psale.sale.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "decreased-retreat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "42"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 历史数据长度\n",
    "g_datalen = len(salesData)\n",
    "\n",
    "g_datalen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sonic-breakdown",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 42 entries, 2017/10 to 2021/3\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   sale    42 non-null     float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 672.0+ bytes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sale</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017/10</th>\n",
       "      <td>0.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017/11</th>\n",
       "      <td>0.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017/12</th>\n",
       "      <td>0.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/1</th>\n",
       "      <td>0.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018/2</th>\n",
       "      <td>0.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sale\n",
       "date         \n",
       "2017/10  0.88\n",
       "2017/11  0.78\n",
       "2017/12  0.59\n",
       "2018/1   0.93\n",
       "2018/2   0.47"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 创建字典对象\n",
    "data = {\"date\":yearAndMonth,\"sale\":salesData}\n",
    "\n",
    "# 创建dataframe\n",
    "sale = pd.DataFrame(data)\n",
    "\n",
    "# # 转换日期数据类型\n",
    "# index = pd.DatetimeIndex(yearAndMonth)\n",
    "\n",
    "# 将日期设置索引值\n",
    "sale.set_index(['date'],inplace=True)\n",
    "\n",
    "# 转换销售量数据类型\n",
    "sale.sale=sale.sale.astype('float')\n",
    "\n",
    "primitiveSale = sale\n",
    "\n",
    "# 查看数据信息|\n",
    "sale.info()\n",
    "sale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "searching-assistant",
   "metadata": {},
   "source": [
    "## 原始序列的检验"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "asian-poison",
   "metadata": {},
   "source": [
    "#### 单位根检验，满足该条件，则拒绝原假设（非平稳序列），说明是平稳序列\n",
    "ADF(sale.sale)[1]<0.05\n",
    "#### 白噪声检验，满足该条件，则拒绝原假设（纯随机序列），说明是非白噪声\n",
    "acorr_ljungbox(sale,lags=1)['lb_pvalue'].values[0]<0.05\n",
    "#### 进行差分运算\n",
    "sale=sale.diff(periods=1, axis=0).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "separate-chase",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------第1次计算---------------------:\n",
      " 单位根检验： (-5.452202573375329, 2.6291350683837115e-06, 0, 41, {'1%': -3.60098336718852, '5%': -2.9351348158036012, '10%': -2.6059629803688282}, -9.19255575578687) \n",
      "白噪声检验：     lb_stat  lb_pvalue\n",
      "1  1.176245   0.278122\n",
      "---------------------第2次计算---------------------:\n",
      " 单位根检验： (-11.158380743434066, 2.828139415198418e-20, 0, 40, {'1%': -3.6055648906249997, '5%': -2.937069375, '10%': -2.606985625}, -2.0975934855361302) \n",
      "白噪声检验：      lb_stat  lb_pvalue\n",
      "1  12.376407   0.000435\n",
      "\n",
      "经历了1阶差分\n"
     ]
    }
   ],
   "source": [
    "d = 0\n",
    "while(True):\n",
    "    print(\"---------------------第{}次计算---------------------:\\n\".format(str(d+1)),\n",
    "          \"单位根检验：\",ADF(sale.sale),\"\\n白噪声检验：\",acorr_ljungbox(sale,lags=1))\n",
    "    if (ADF(sale.sale)[1]<0.05) and (acorr_ljungbox(sale,lags=1)['lb_pvalue'].values[0]<0.05):\n",
    "        break\n",
    "    else:\n",
    "        d = d+1\n",
    "        sale=sale.diff(periods=1, axis=0).dropna()\n",
    "print(\"\\n经历了{}阶差分\".format(str(d)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "descending-secondary",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  FutureWarning,\n",
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:2: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acf(sale,lags=g_datalen-1-d).show()\n",
    "plot_pacf(sale,lags=g_datalen/2-1-d).show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "union-television",
   "metadata": {},
   "source": [
    "## 建模及预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "knowing-moisture",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\base\\model.py:606: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0, 1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:539: ValueWarning: No frequency information was provided, so inferred frequency MS will be used.\n",
      "  % freq, ValueWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>SARIMAX Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>sale</td>       <th>  No. Observations:  </th>   <td>42</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARIMA(0, 1, 1)</td>  <th>  Log Likelihood     </th>  <td>7.733</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Sat, 20 Nov 2021</td> <th>  AIC                </th> <td>-11.466</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>02:13:00</td>     <th>  BIC                </th> <td>-8.039</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>10-01-2017</td>    <th>  HQIC               </th> <td>-10.218</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 03-01-2021</td>   <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>    <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.8007</td> <td>    0.118</td> <td>   -6.768</td> <td> 0.000</td> <td>   -1.033</td> <td>   -0.569</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0392</td> <td>    0.010</td> <td>    3.748</td> <td> 0.000</td> <td>    0.019</td> <td>    0.060</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.08</td> <th>  Jarque-Bera (JB):  </th> <td>0.85</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.78</td> <th>  Prob(JB):          </th> <td>0.65</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>2.38</td> <th>  Skew:              </th> <td>-0.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.12</td> <th>  Kurtosis:          </th> <td>2.39</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                               SARIMAX Results                                \n",
       "==============================================================================\n",
       "Dep. Variable:                   sale   No. Observations:                   42\n",
       "Model:                 ARIMA(0, 1, 1)   Log Likelihood                   7.733\n",
       "Date:                Sat, 20 Nov 2021   AIC                            -11.466\n",
       "Time:                        02:13:00   BIC                             -8.039\n",
       "Sample:                    10-01-2017   HQIC                           -10.218\n",
       "                         - 03-01-2021                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ma.L1         -0.8007      0.118     -6.768      0.000      -1.033      -0.569\n",
       "sigma2         0.0392      0.010      3.748      0.000       0.019       0.060\n",
       "===================================================================================\n",
       "Ljung-Box (L1) (Q):                   0.08   Jarque-Bera (JB):                 0.85\n",
       "Prob(Q):                              0.78   Prob(JB):                         0.65\n",
       "Heteroskedasticity (H):               2.38   Skew:                            -0.18\n",
       "Prob(H) (two-sided):                  0.12   Kurtosis:                         2.39\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = d = q = range(0, 2) \n",
    "pdq = list(itertools.product(p, d, q)) \n",
    "pdq_x_PDQs = [(x[0], x[1], x[2], 12) for x in list(itertools.product(p, d, q))] \n",
    "a=[]\n",
    "b=[]\n",
    "c=[]\n",
    "wf=pd.DataFrame()\n",
    "for param in pdq:\n",
    "    for seasonal_param in pdq_x_PDQs:\n",
    "        try:\n",
    "            mod = sm.tsa.statespace.SARIMAX(primitiveSale,order=param,seasonal_order=seasonal_param,enforce_stationarity=False,enforce_invertibility=False)\n",
    "            results = mod.fit()\n",
    "#             print('ARIMA{}x{} - AIC:{}'.format(param, seasonal_param, results.aic))\n",
    "            a.append(param)\n",
    "            b.append(seasonal_param)\n",
    "            c.append(results.aic)\n",
    "        except:\n",
    "            continue\n",
    "wf['pdq']=a\n",
    "wf['pdq_x_PDQs']=b\n",
    "wf['aic']=c\n",
    "print(wf[wf['aic']==wf['aic'].min()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "opposed-chambers",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "american-adobe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1154e129358>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1154e145a20>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1154e1524a8>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1154e0c6588>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '奥迪Q5汽车销量预测')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nh88++4wbb7yxwt0YlVmyZAlLly5VPzCnTp2K0+mkffv2DB8+HPDdxfHVV19VOPaBBx5g69atlbar1+vJycmp9Bv6Aw88wAMPPFBhfU2vZxg9ejR9+/atcBrF//tRWUH0d3fffTeFhYXcfffd6rr09PSAYu74u06kaBDhIEM4i5Oa2+0mJSWFlJQUPvroIzZt2qR+O500aRI33XQT4OsZePfdd7n88stJTEykZ8+eNfpPu6b/sR9/usL/AeO/DbCkpITBgwfzySefkJOTg06nq/LD139twsGDByts279/P3fffTf/93//V21MsbGxDTanwosvvkheXh7//ve/AV+c+fn5ag8D+D70TznllAqPv19YeLyysjKSk5M5ePBgwCM2NpYZM2YErHvyySeBmr9nzZs3r9C75I+9RYsWAQVgZUVlXl4eH374odqj06lTJ/bt28eBAwfo2bMn0dHRaiHibz/YMNFC1CfpURAnrfT0dAoLC/n666/VLm3/h6vT6eTgwYP84x//qDCEc7du3fjggw84ePAgNpuNDh06BH0Oj8dDUVGR2oa/CzwrK0tdV1RUFPCB3Lt3b+DYRY1arZa8vDzuv/9+9u/fT48ePdDpdDz22GPY7XaeeuqpgOf0n+dPSUmpEM/dd9+NRqPhpZdeqvb1adasWaVzUmRmZjJz5kwMBoN6quHvfvzxR8D3rf3vPSb+MRK6du3KPffcE/T5PR5PQNu1OfWg0WgoKSnhlFNOCVhfUlLC008/rRYmfnFxceqpn1AcP/7Cjz/+WOH5KhufYd68eZSVlfHYY4/Rq1cv9uzZo8Zz7rnn8tdff6mv48qVK4O2I0S9C+vNmUKE0cyZM5WYmBglMTFRadasWcAjJiZGvQc+Ojq6wvakpCTFZDIpM2fODGizT58+Su/evRWPx6MoiqIkJydXeX+9//H3sRA6deqkxMbGquMoLFq0SN3XP6rilClTlOjo6IARGIuLi5Xzzz9fAZRNmzYFtOkfTXH69Ok1en2efPJJBVAKCgoC1m/btk2JiopSjEajEh8fX+G1adasmWI0GhVASUpKqrAtISFBiY2NVYYOHVrhOf1jVBQWFiqDBw9Wjh49qiiKotx///1Ky5YtA0az9D+uuuoqpVOnThXacrvditVqVZxOZ4VtcXFxyjPPPFNhf7vdrpSUlAR9TX744QcFULZs2RKw/p133lFmz56tHDlyRImLi1NmzZqlPu8777yjfPnllwH7Z2RkKHFxccqYMWMCnr9Lly5Kr169lFmzZilarVb56KOPKsQg4yiIhiaFghB/s2fPHqV58+bK2LFjlWnTpikWi0VZvXp1tcfNmzdP/TCfMmWKOlhQbaxcuVLRarVKx44dlQULFigPPfSQ2rZ/OOV9+/YpFotFadasmXLzzTcrt99+u9KxY0cFUO64446A9g4cOKAkJSUpLVu2VGw2W41iWLdunQIoX3zxhbJ9+3Zl1qxZFYqGYGbOnFlpkVGdxYsXK4BywQUXKICybNkyRVF8hUK7du0qPWbs2LGVFgr+QYxCffiHja5MsELBb+rUqYpGo1G2bdumDBkyRJk7d26l+w0fPlzRarXKzp071XXz589XAOWDDz5QysrKlHPPPVcxmUwB+yiKFAqi4UmhIEQ5h8OhvPbaa0qzZs2Uc889V7FarYrX61Xuv/9+RaPRKOPHj1e2b99e6bGfffaZotVqlX79+ilXXHGFAihDhw5Vtm7dWut4Vq9erc71kJCQoPTv318BlEWLFqn7bN26VRk2bJhiNpsVs9msnH/++cr7778f0E5+fr5yxhlnKIDy1ltv1fj5XS6X0qpVK+Xaa69V3nzzTQVQ1q1bV6Nj/YWNv0egpvwFhlarVV544QV1/X333Re0ULj66qsrLRSsVquyc+dOxWq1VpjXIS4uTpk7d27AurKyMqWwsLDK4sY/58Jvv/1WYdtrr72mAMpdd92luN1uZdq0aYpGo1EmTpwY0KuxdOlSBVDnEVEURdm8ebMSExOjnHfeeeq69PR0JTY2VjnzzDMVp9OpWK1W5eeff1bGjx+vAAGjdgpRn6RQECetI0eOKD///LOyePFiZdy4cUqzZs2U6OhoZerUqYrD4QjYd+XKlUrnzp0VQOnZs6dy++23K4sXL1Y2bNigFBYWKomJiUqrVq2U/Px8RVEUZcmSJUqLFi0UQGnZsqVyySWXKBMnTlTuv/9+5ZFHHlGeeOIJZc6cOcr999+v3HTTTcrw4cOVc845J2ghoii+D+6HHnpIHfa3pn777TfllFNOUc4777yQezn8pzxiY2OVtm3b1vi5p0+frgBKTk5OSM93/vnnKxqNRlm8eHHA+rvuuqtCobBixQrl4osvVnQ6nXL66acHbEtPT1f++usvJTMzs9KhmmNjY5WHH3640m0ZGRnKrl27FLvdrra3evVqZciQIUqHDh0UrVYbkFd+fr4yZcoUBVBGjhwZ8Lvz3nvvKXq9XhkyZIjanv99/P777xVFUZSSkhKlW7duSkJCgrJnz56APJ5++mnl3nvvVZxOp1JUVKTExcWpv4Mn0mMlRCikUBAnrZtuukntbu7evbsya9asCrMkHs/pdCrvvvuuMmzYMPU/bP855hUrVgTMpaAoilJaWqqsWLFCmTZtmnLZZZcpvXr1UlJTU9Xz939/HD/fQ11zOBxKZmZmrY59/vnnlX79+in/+9//anzMnXfeqQDKgQMHQnqub775RpkzZ06F9ZMnT65QKOzbt08BlNTU1Ao9JRdccIE662Zl11BU9TCbzYperw+YP+Lw4cOK0WhUevToEdDT8fvvvyvx8fGKXq9X5syZU+mH94oVKxSdTqfMmzcvaN55eXnKxo0bq3193nnnHWXNmjVKWVlZtfsKUVc0ilI+vJwQJ5kDBw7w22+/0a9fvxrd8348j8fD1q1bMZlMdO7cOeTnVhQFp9Op3hYZFRWFXq8PelW/qFx6enqVd500hHXr1pGSklJhyO3j/fLLL+qtlEI0NVIoCCGEECKokMdRsFqtPPDAA8yaNavS+7SPt2zZMpYvX15h/axZs+jZsyfTpk0jIyNDXT9w4EAmT54cakhCCCGEqCchFQpWq5W5c+eqI8ZV56qrruKf//ynupyXl8djjz1Ghw4dKCsrIycnh1dffVUdVMU/25sQQgghGoeQhnCeP38+AwYMqPH+BoOBuLg49bF69WqGDh1KbGws6enptG3bFovFom6vbBIcIYQQQoRPSIXCzTffzNChQ2v1RPn5+fz888/q8Xv27CE/P5+JEycyYcIEFi1aVKNJdoQQQgjRcEI69VDdNQlV+fLLLxkwYIA6M97hw4fp1q0bo0aNori4mAULFvDZZ59x1VVXBW3D5XIFFBNarTZgznohhBBC1K0GmRTK6/Xy1Vdf8fDDD6vrJk2aFLDPNddcw6pVq6osFD7++OOAiyO7du3KY489VufxCiGEEMKnQQqFbdu2YTabad26ddB94uPjyc/Pr7Kd4cOHM2zYMHXZf895Xl5eg02HW5c0Gg2pqalkZ2cTKXepRmJOfpGYWyTm5BeJuUViTn6RmFtjz0mn05GcnFz9fg0QCz/88APnnHNOwLqHHnqIu+66i+bNmwOwa9euagPW6/VB74xojG9CTSm+ETLDHUadisSc/CIxt0jMyS8Sc4vEnPwiMbemnlNIFzMGU1JSUuU3+t9++42ePXsGrGvTpg2LFi1i9+7drF+/npUrV3LJJZfURThCCCGEqCN1Uijce++9bN68udJt2dnZFBQUVBjmdty4ceh0OubMmcMHH3zAuHHjuPDCC+siHCGEEELUkVqdeli2bFnA8ksvvRR039TUVN5///0K6+Pi4rj33ntr8/RCCCGEaCANco2CEEKIyOR2uykpKamTtkpLS3E6nXXSVmMR7pxiY2PR6U7so14KBSGEELXidrspLi7GbDaj1Z74mWy9Xh9xA++FMyev14vNZiMuLu6EioU6uUZBCCHEyaekpKTOigRR97RaLWaz+YR7fOTdFUIIUWtSJDRudfH+yDsshBBCiKCkUAgjxesNdwhCCCGC8Hq9AdcX/PLLL+zZs6fCfjt27KiX5y8oKKC4uLhe2g6FXMwYJmUlVoa+3JvuSjNeuu/XcIcjhBAnlSFDhpCVlYXRaAy6j9fr5ZprruH+++8H4N1336Vbt24B4wLt37+fIUOGsGrVKk499dQKbbzwwguMHj2aN954g4KCAqZMmcLSpUuZOnUqEydO5Prrr2fgwIGVPv/s2bPp06cPN9xwA8888wyrVq2qdL/ly5eTlJQUSvohkUIhTDL/+pGd8U72eLJ40euF8nkrhBBC1D+9Xs/TTz/NpZdeqq679tprefrpp2nbtm2lxxw6dIhrr702YN2SJUsYM2YMp556KoWFhSQkJARs12q13HvvvZx22mkYDAbee+89NBoNHo+HDRs28MADDwSNMSYmhujoaACOHDnCnXfeyZVXXhmwT9++fTEYDKGkHjIpFMLEbssDwB0FpfYCjOb6qwaFEEJULTs7m61bt5KWllZh2y+//MKaNWvYsWMHK1euZP369Vx22WW0bt2azz//nE8//RRFURg7diw33HADo0aNAnynDlq3bo3T6WTv3r0UFxfTokULOnfuzPfff4/ZbFZ7JzweDx6PB4PBwKZNmzhw4AD79+9Ho9Gg1WrxVnGqWlPPXzSlUAgTu+2o+nNxQZYUCkIIEQbvvvsujz32GGlpabRo0YIhQ4YAYLPZSE1NZeXKleoYBB6Ph9TUVD788EPOOOMMFi9ezEMPPURCQgKKovDwww9zww030LNnT3r27InNZmPt2rUcOnSIDRs20LNnTxISEtiyZQs7duyguLiYvn37UlJSgsPh4JZbbuGuu+5i//79bN68mSNHjqh3LXi9Xh599FHmz59PQUEBubm5dOvWrcJ1FPVBCoUwsZccm1LbVpBN87Y9q9hbCCGaAEVBU1pa++N1OjRVTDAY9GmNxlqfvtXpdFx44YUsXLgwYP2nn37KG2+8AcDpp5+O2+3mt99+47bbbuPLL79k9+7dfPDBB3z55ZfcfffdGI1GEhMTad++PZMmTWLdunW0bduWW265hYkTJzJw4EASEhLIzMzklVdeYciQIcycOZPrrruON998k127dnHXXXcBMHLkSEaOHMn9999P79690Wg0bN26lZkzZ3LllVeybds2ZsyYwYoVK2qVc8ivUYM8i6jAVlKg/lxsOxLGSIQQom5oSktJ69KlwZ83a/dulNjYkI/zd9mvX7++wgWFNpuNdu3aqct//PEHvXr1AmDv3r1cd911xMXFcfbZZ9OpUydiYmLUfdesWUN0dDTffvst999/PwsWLODXX38lPz+fnj178uCDD5KRkcGBAwcAyMnJoUWLFgHPrygK+fn5PPLII4waNQqHw8GMGTN44okncLvdFBQU0LdvXxRFISUlhU8//TTk/GtKCoUwsTmK1JtTbeXXKwghhGgYpaWlxMXFUVhYGLRHYfHixeryr7/+yuDBg9mzZw9paWkkJiYyYcKEStsePHgwAP3792f16tXYbDamTp3K6tWrMZlMPPvss9xyyy1s2rQJgIyMjICLKjdt2sRjjz3Gr7/+ysMPP8ykSZO4+eabWbhwIeeddx47d+5kxowZLF++HEVRcDgcdfzqBJJCIUxsTiuUF6C24vyqdxZCiCZAMRrJ2r271sfrdDrctT31EKLc3FySkpI4cuQImzdvpm/fvpSWlmK320lOTgage/fu6v4///wzw4cPZ8OGDQwYMACAiRMnsmbNmoCLCT0eD2+//TYDBw7k008/5ZFHHsFms2EwGBg0aBCFhYVcffXVPPHEE/Tt25e8vDw2bdrEjBkz1Da++uor2rZtS8eOHYmPjwegsLCQ5s2bV8hDo9FUeYtnXZBCIUzsruJjhUKJFApCiAig0dTqFIBKr0dpgAmU8vPz1TsSevTowfnnn0+zZs3U6xKWL19OUVGR+iEN8MQTT/D888+TkZHBSy+9VB6unnnz5jF69Gh1v4EDB6q3NF511VVcddVVnH/++bz11lt06NCBSy65hBEjRqDRaLjqqqu49dZbsVgspKamqm3ccccdxMbGquM3AOzZs4ft27dzzTXX0KZNGwAuu+wydu3axbp16wJOk9Q1GZkxTKyeY6Nt2RxFYYxECCFOLhs2bKBHjx5YLBYOHTrEhRdeyJ9//qluLyws5B//+Adffvmluu6SSy7hggsuoH379kyfPp2vvvoq6G2Jx6//66+/aNasGbfddhv/+te/UBSFM844A4Bx48axcePGgEIDfFNDH+/QoUOkpaVhsVi4+OKLWbVqlfro2LEjer3+hF+TqkihECY277Erg21OaxgjEUKIk8snn3zCBRdcQGZmJjfccAM33ngj3bt3R1EUABISEnj55ZeZOnUqX3zxBQBLly7l008/5d133+Xll1/GaDSiKAoPPvgg3bt3Vx+7d+9W2wE49dRTWbFiBVdffTXbt2+nXbt2TJgwgYKCAqZPn865557LwoULKx0a2uPxYDQaWb16NZdccknQfI5/vvoghUKYWJVjF5/YXPYwRiKEECeP7du3s3r1akaNGsXHH3/M5Zdfzt13382iRYt44okn1JEV+/fvzyuvvEJ+fj5//PEHr776Ku+99x5ms5nevXvTv39/3G43TzzxBH/++af66NKlC06nE/Cd4njllVe47LLL2Lx5MytXrmTRokWcf/75XHzxxfTr14/ly5czYcIEhg0bxieffKLG+fLLL+PxeOjXrx8fffQRo0ePxul0smbNGgYOHKg+9u3bpz5ffdEo9V2KNIC8vLx6H3Cirl31dBc2JfrmCB9X2IGn7t0Q5ojqhkajIS0tjaysrHqvchtaJOYWiTn5RWJujS0nq9WKxWKps/b0en2D/F++fft2evYMHLtmx44d7Nixg4EDB1Y6b4KiKBVONZSUlKDT6YIOoex2u/nvf//L4MGD6XLcbaPvvvsuZ511Ft26dVPXffXVV5xxxhlVztngdrvxeDzqNRA1Fex90uv16oWbVZGLGcOkSHvsj8HqLQljJEIIcXL5e5EA0KNHD3r06BH0mMquR/j7tQR/p9PpmDp1aoXi5/rrr6+w76BBg6psy9+ef5TIhiSnHsLEpvOoPx9/GkIIIYRoTKRQCJMi/bEJPqza+j2/JIQQQtSWFAph4CoroeS4U1o2begDjAghhBANQQqFMCguzA1YLtJLoSCEEKJxkkIhDOwF2QHLRfrwX70shBBCVEYKhTAotvp6FKLLOxIcenCWylgKQgghGh8pFMLAP1tk65Jjt7nYC7LCFY4QQpx0Pv/8c95//33ANwJiaWlpjcamOHDgALfeemvQ7du2bWPjxo11EmNBQQHFxcXV71jPZByFMLDbfZNAJXj0mJxu7AYoLswhqWXDz+MuhBAnI4vFwu23307Pnj3RarVcf/31GAyGgPESHA4Hbdu2Zfny5Rw9ehTwjWVw5MgRfv31V9LS0tS2TCYTAOvXr2fHjh3qDJMvvPACo0eP5o033qCgoIApU6awdOlSpk6dysSJE7n++usZOHBgpTHOnj2bPn36cMMNN/DMM8+watWqSvdbvnx5lQM1nSgpFMLAXj5bpMVrIN5Zht3gxW7NreYoIYQQdeW8887j6aefplWrViQlJTF79mycTiejRo1S9/n666958cUX2bx5M9ddd13Ah/GkSZMAsNlsTJs2jTVr1jB58uQKIzVqtVruvfdeTjvtNAwGA++99x4ajQaPx8OGDRt44IEHgsYYExOjjsJ45MgR7rzzTq688sqAffr27Rt0ZMi6IoVCGNhKCwEwa2KI9zg4RBk2a154gxJCiJPETTfdxMUXX8wVV1yhjnS4ZMkSRowYAfhORfjnT9BoNCiKQp8+fVi+fDkAt99+OzNnziQlJYXHH38crVaLwWAgJiYm4Hn8U1k7nU727t1LcXExLVq0oHPnznz//feYzWY6d+6sPqfH48FgMLBp0yYOHDjA/v370Wg0aLVavF4vwQSbxbKuSKEQBrYyK+jAojViUYqBMmz2o+EOSwghToiiKJS6S6vfMQgdOtyu0G8XN+qMIX1Y+q9HmDFjBjt27CAnJwePx8N//vMfHn74YTp16kSnTp24+uqrATj33HO55ZZbuOaaa4iKimLTpk1kZmai0+kYNWoUI0eO5Ouvv0arDbzsz2azsXbtWg4dOsSGDRvo2bMnCQkJbNmyhR07dlBcXEzfvn0pKSnB4XBwyy23cNddd7F//342b97MkSNH1Da9Xi+PPvoo8+fPp6CggNzcXLp164bX6633+TFCLhSsVisPPPAAs2bNIiUlpdr9586dy6+//qou9+rVi5kzZwK+STgWLVqE1Wpl+PDhDBs2LNRwmiS70wY6MEfFEu+JBuzYyk9HCCFEU1XqLqXLGw1/rdXuCbuJ1Vc978LxtFotGo2GZ599llWrVvHiiy9y66230qVLF6ZPn86oUaMYPXo0X3/9NeD7xt67d29mz56NwWBgzJgx3HvvvSQmJqqzTVambdu23HLLLUycOJGBAweSkJBAZmYmr7zyCkOGDGHmzJlcd911vPnmm+zatYu77roLgJEjRzJy5Ejuv/9+evfujUajYevWrcycOZMrr7ySbdu2MWPGDFasWHEiL1uNhVQoWK1W5s6dS15ezbvJ9+3bx7x582jWrBkAUVFRAW1dfvnlDBgwgOeff5727dtz6qmnhhJSk2R1+26FNOvNxHttwFFsjqLwBiWEECeRrVu38vrrr7Nv3z6mTZvGm2++SUpKCv/85z95/PHHWbhwIddcc426//79+1m2bBlRUVHY7XaWLVtGTEwMgwYNUi9q/Ltvv/2W+++/nwULFvDrr7+Sn59Pz549efDBB8nIyODAgQMA5OTk0KJFi4BjFUUhPz+fRx55hFGjRuFwOJgxYwZPPPEEbrebgoIC+vbti6IopKSk8Omnn9bbaxVSoTB//nwGDBjA7t27a7R/fn4+iqLQtm3bCtu+++47kpKSGDFiBBqNhmuuuYZ169adFIWCzeObLdJsMBPviQPAXmYNZ0hCCHHCjDojuyfU7POhMjp97U89hKpbt24MHjyY66+/npYtWxIdHU10dDRut5tWrVrx3HPPcfjwYdavX89PP/3Exx9/HDBzo0ajISoqii+//JJ27dpV+hz9+/dn9erV2Gw2pk6dyurVqzGZTDz77LPccsstbNq0CYCMjAwuvfRS9bhNmzbx2GOP8euvv/Lwww8zadIkbr75ZhYuXMh5553Hzp07mTFjBsuXL0dRFByO+p1YMKRC4eabbyYlJYU33nijRvvv2bMHr9fL5MmTKS4upk+fPvzrX//CZDJx4MABevbsqZ5X6ty5M0uWLKmyPZfLFXAuRqPRYDQa1Z+bCpviO4dniUkg3l0IgNVla1I5BOPPIRJy+btIzC0Sc/KLxNwae04ajSakUwB/p9frcVG/59v9DAYDF1xwAV6vl5deeonDhw8THR3N5s2biY6Opnv37mRn+0bR7dKlCzfeeKPaI/7FF18watQokpOT8Xg8tGzZstLn+PTTT3nkkUew2WwYDAYGDRpEYWEhV199NU888QR9+/YlLy+PTZs2MWPGDPW4r776irZt29KxY0fi4+MBKCwspHnz5hWe4/jPwaqcyO9MSIVCTa5JON6hQ4do164d48aNQ6vV8p///IclS5YwadIkSkpKaN26tbqv0WgkP7/q8/Qff/yxetUpQIcOHZg7dy7JyckhxRVudo3vD6FF85YUufJBgVLKgnZfNUWpqanhDqHeRGJukZiTXyTm1lhyKi0tRa/X12mbdd1eZbRaLTqdDr1ej1ar5dZbb2XZsmWkpKTQtWtXVqxYgV6vR6fTodFoaNGiBStXruSdd97BaDTicrl49NFH8Xq9DBgwgEceeQSn04nH40Gr1aLVatHr9YwcOZKrrrqKCy64gHfffZeOHTty0UUXMXr0aAwGA1dffTW333478fHxtGnTRo3v7rvvJi4ujmnTpqlx7t27l507dzJy5Eh136FDh7Jr1y6++eYb2rdvHzRfg8FwQp8v9XrXw/Dhwxk+fLi6PHbsWJ599lkmTZpEVFRUQDeOwWBQb0epqr3jL3j0V0h5eXm43U1nYqVCygDQEkN8TAKUQn6Zlayspj86o0ajITU1lezs7BqNctaURGJukZiTXyTm1thycjqddXrFvV6vr/cr+MF3K6Lb7VZHPczPz8fpdFJUVERMTAxXXXUVmZmZuN1uFEXB5XJRVFRE7969mTdvntrOW2+9xa+//orL5eKee+6hY8eObN++PeBOhL/++oukpCQmT55MWloaXq+XXr164XK5uP7663nhhReYM2dOQN4GgwGXy4XX68XtdrN//35SU1OJi4tj0KBBPP/88+q+l1xyCRqNpsrXzel0Vvr5otPpavRFu0Fvj4yPj8dms+FyuTCZTFitx87Ll5aWBhQOldHr9UGrzcbwR1NTtihfUWMyNSfemQ+lYKVmw4c2FYqiRFQ+x4vE3CIxJ79IzC0Sc2pI/g9Vg8HA9u3bsVgsxMTEMH/+fA4ePIjNZiMuLo6ioiL19sRgXff+9f369QPA7XarpygATj31VFasWMFrr73Gq6++So8ePZgwYQILFixg+vTpnHvuuSxcuJALL7xQHVPBz+PxYDQaWb16NZdccknQfGryu3Aivy/1OtfDv//9b3bu3Kku79q1i/j4ePR6PZ06dQq4KDI9Pb1eh6BsTIr0HgBMlubEm3znnGxU3ZsihBCibhz/7dtisQAwatQoNm7cyBdffMHGjRvR6/XMmzdP/YB2uVysWrWKgQMHqo/58+cHtPXMM8+wceNGhgwZAvh6Kl555RUuu+wyNm/ezMqVK1m0aBHnn38+F198Mf369WP58uVMmDCBYcOG8cknn6htvfzyy3g8Hvr168dHH33E6NGjcTqdrFmzJiCGffv2Vdsbf6LqpEehpKQEg8FQoUegbdu2vPnmm4wfPx6r1cqSJUsYPHgwAGeddRavvfYaf/zxBz169OCTTz6hd+/edRFOo6Z4vVgNvsrOFN8Ct7MAgCJtw1zAI4QQJ7sPP/yw2n2GDx/OFVdcoY62eNNNN/F///d/VY6bcO+99wYsWywWSkpKWLBgAV26HBtfIi4ujiVLltCtWzfAN9Jjjx49OOOMM9R9brnlFvVn/62PQ4YM4eKLL1aHdW4odVIo3HvvvYwfP55zzjknYP2VV15Jbm4ujz/+OEajkSFDhqjXLFgsFsaPH8+TTz5JTEwMcXFxAS9MpHIUF+Eq75UyJabiLfNdwGnVNZ1rLIQQItL9/VS32WwOuQ2dTsfUqVMrXD9w/fXXV9h30KBBNWqvulP09aFWz7hs2bKA5ZdeeqnyxnU6pkyZwpQpUyrdPnjwYE4//XQOHTpE9+7dK4yTHYlKCnMA0CgQG5+Mxum7ErXIEHwcbyGEECJcwj7XQ0pKSsi3XTZltkLffblmJ2ijdMQ3891/azeAx+UkSl+/s4AJIYQQoajXixlFRcXls0TGl59/iE8+du9scXlvgxBCCNFYSKHQwGw2X6FgdvsKBYPRREz56St7eW+DEEII0VhIodDA7CW+uxzivccukol3+e7DtUmPghBCiEZGCoUGZiv23eVg5tjtLRa371IRu63ms3IKIYSoO6WlpRVG+PV6vZSW+ubmKSkpqVE7hw8f5q+//qrz+MIp7Bcznmz8s0RaOHaHh8WjA1zYbEfCFJUQQpw8Tj/9dMxmMzExMdhsNoYNG8b27dvJzs6muLiYoqIi2rZti9frJTExkWXLljFs2DAGDx7Mfffdx4svvojXe+xOte7du6uDLH3++ef8/vvvvPDCC+r2J554gvbt23PdddcFxPH888+zf//+gCGZjzdgwAA8Hg+xsb6Jtvbu3Uvbtm3V2zb37t3LmjVr1PEY6osUCg3M5iiCaDBFHZthzeI1AKXYS6qeFEsIIcSJ++233wDYv38/V1xxBaNHj1YHRHr33Xf56aefWLBgQcAxr776KpMmTWLQoEG89NJLzJo1C4BffvmFw4cPk5qayqFDhzAYDAFDOOfk5PD222/z/vvvA74hnv0TR/mntgZf74XT6QwYJkCv1/PMM8/Qv39/wFfgLFmyRJ0U6vTTT2+QwZekUGhgNrcdosGii1PXWTQxQBG20sKwxSWEECeLkpISnnrqKQ4dOsT06dMDRk3My8ujQ4cOFY7p2LEjq1evJioqCr1ez6WXXsqGDRvo27cvmzdv5s8//2TTpk0VRhieNWsWEyZMoFevXrz99tscOXKEN998k+joaOx2O263m/Xr1+P1enE4HHz77bckJiYCvlku7777brVHoaCggOuuu07tUSgoKAgoSuqLFAoNzOr2zVZm1pnUdRat75fAVmat9BghhGgKFAVKSyufPKkmdDpwu0M/3mhUCDJnU6X0ej25ubn8+OOP6oCBP/30E3fccQcejwedTsfKlSspLS3lwQcf5LTTTmPJkiXcd999ahs2m4377ruPOXPmAL7Jof4+cdQrr7zC2rVr+fe//82TTz7JO++8w+eff85dd90FwKJFi9i/fz+PP/54pXF6vV6ee+65KnsUGmJyMCkUGpjd47sgxhwTr64zl5+GsDmlUBBCNF2lpRq6dElr8OfdvTuL2NiafWB6PB68Xi8vvvgizz77rPpBq9FoaNOmDcuXL+e3336je/fuTJs2Da/XS0xMDBs3buTGG2/ktddeA3wjD1c3rHN0dDTjxo3j+++/57XXXuPf//43HTp0oH///ng8HrVHYe3atXg8Hh5++GGuuOIK9fjjr4MI5u8XYNYHKRQamBUHAKZoi7rOrPf1Llhd9rDEJIQQJ4tt27Zx6623oigK2dnZrF69mt27d/POO+8AvumYhw0bxtatWwGIioqiRYsWvP/++7z77rtqt7+/gKjK+PHj0ev1jBkzhoEDBzJq1CgAVq9eTWxsbMBpA5fLVaF3oG3btsyYMUNd/vuph6SkpKDTX9clKRQamI0yAMxxx6bUtkT7ehes3prdfiOEEI2R0aiwe3dWrY/X6XS1+oZsNNa8+713795s2LCBPXv2MGXKFP7zn/8wadIkTCbfF7bS0lLMZrN6nYCfyWRiyJAhKIqC1+vlyJEjNGvWDKj6m/+qVav48ccf+frrr9V1lfVEHD8BVUlJCU6nk7feegut9tgoBn8/9QC+HgWn04nBUH/D/0uh0MD800mbYo/9Eppj4qEM7IojXGEJIcQJ02io8SmAyuj14HLV/zn34yUnJ/PSSy9ht/t6dI8ePUqLFi3U7ccXAVOmTGHChAk4HA527dql7udwOFAUBY/HE9D2t99+y5QpU+jRowcvv/wye/bsYcGCBTzwwAP8/PPPxMUdu6j98OHDfP3113Tu3JlPP/2Uxx9/nOjoaDQaDQ6Hg6KiIjweD4MHD8bj8aiFTElJCVOmTKnX2ZdlwKUGZtP5fpHM5mR1nTnW17tQVN7bIIQQomFYLBZOOeUUnE4nAFu2bAm4C6KszPf/8qZNmzh06BCXX345P/30E1999RVnnHEGffr0Yd68ebjd7grTSZtMJvr168c555zD6aefzt13301KSgrR0dHMnDmTn376SX00a9ZM7RUYNWoUv//+Oz///DNr1qyhTZs2PPLIIzRr1ow333wTs9nM4sWL+emnn9i6dWu9FgkghUKDK9L7CoU4y7FCwd+7YNU6wxKTEEKc7Hr06MHs2bN58803GThwIP/73/+YOXMml112GQDz5s3jlltuITY2FpvNxldffUVycjIjRowgJyeHsWPH8uKLLwa0eeaZZ/L+++/zwAMPcO655+JyucjLCz4C7/HXGyiKwieffMIll1zC4MGDueGGGwBIS0tj3rx5jB49mmeffZbs7PqfI0gKhQbk9bixlZ9GMiekquv9vQvWqPq/elUIIU52NpuN7OxsdLpjZ99NJhNLly7F6XRy2WWXsXz5cu699148Hg9Lly7ljz/+YNy4cbjdbh566CHGjBnDiBEjGDNmDLfddhsej4eysjKOHDmifuA/8cQTnHnmmXTq1Ikrr7ySl156iZKSEhRF4dFHH6Vv377q4+jRo2osmzZt4txzz+XFF1/kxRdf5I477gB8p0HcbjcXXXQRq1atYv/+/fTv35/58+fX6+sl1yg0oJKiPJTygjE24dg5MFN574K/t0EIIUT9WbhwIR988AG33nor4Bupcdq0aXTs2JH33nsPk8nE22+/zbx58xgxYgRvvvkmc+bMIS4ujk8//ZTDhw/zyiuvADB16lRKSkpwOBw8//zzLF68mKeffhqAK664gosuuoju3buTkJCgPr/T6WTmzJmMHj1aXXf66aerpz9OO+005s6dyz/+8Y+AuN1ut3oqpE2bNrzwwgvMmjUr4ILH+qBRGmK0hnqWl5dX4dxQY5S1ZzNnfX05eg+kTzqINiqKtLQ0/vjxS3qvHozWCxk3HURTz296fdJoNKSlpZGVldUgA4E0pEjMLRJz8ovE3BpbTlarFYvFUv2ONaTX68Pyf7nH4+H333/nzDPPrLDt0KFDtGrVKmCdw+Go9NbI4uJidDpdwLDKleWUn5+P0WjEaDTWUQZVC/Y+6fV6kpOTKzkiUNP9RGqCiq25AFicmoBiIC7RN0CJVwulVpkYSgghGlJUVFSlRQJQoUgAgo6fEBcXV6O5F5KSkhqsSKgLUig0IJvVdxFLvCtwbG6jKZHymyGw5df+HmQhhBCirkmh0ICK7b6LVcyewEtDNFotFqfv4gV7UU6DxyWEELXRGE5/iOqd6PskhUIDshX7CgWLUnEErXi3762wlZ+eEEKIxk6n01FcXCwFQyOlKIp63cSJkLseGpDdUQSAhYrnsCxuPeDBbpNrFIQQTUNcXBxlZWXYbLY6ac9gMKhX/keKcOcUHR1do+smqiKFQgOyOYpAC2ZNxQthfL0MDuwlBQ0fmBBC1FJdfBBB47ujoy5ESk5y6qEB2Zy+qtsSFVdhm7+XwVYqhYIQQojGQwqFBmRz+QoFs65ioeDvZbCVn54QQgghGgMpFBqQzeObRtpsqDjwhb+XwVpmbdCYhBBCiKpIodCArN5SAEzRFecit+h8c6Hb3PYGjUkIIYSoihQKDcimOACwGBMrbDNH+3oZrO7iBo1JCCGEqErIdz1YrVYeeOABZs2aRUpKSrX7r127lmXLlmGz2ejatStTp04lMdH3QTl37lx+/fVXdd9evXoxc+bMUENqMvzTSMfFVlYoxIMbbEppQ4clhBBCBBVSoWC1Wpk7d26V82kfb+fOnSxdupTbb7+dVq1asWDBAt5++211ysx9+/Yxb948mjVrBvjG245kNq1vGmmzuXmFbWZjAtjAiqOBoxJCCCGCC+nUw/z58xkwYECN98/KyuKmm27itNNOo1mzZlx44YWkp6cDvtmzFEWhbdu2xMXFERcXF3SijUhRpPcVCiZTswrbTHFJAFg1kTXYiBBCiKYtpB6Fm2++mZSUFN54440a7X/RRRcFLB8+fJi0NN9MiXv27MHr9TJ58mSKi4vp06cP//rXvzCZTKGE1KRY9b4BN0zxLSpsM5t8vQzWKHeDxiSEEEJUJaRCoSbXJARjt9tZu3atetrh0KFDtGvXjnHjxqHVavnPf/7DkiVLmDRpUtA2XC5XwLzeGo1GnapTo9HUOraG4CoroVTv+9mU2AKNRqPGrNFoMMf7XtsivafR51KV43OKNJGYWyTm5BeJuUViTn6RmFuk5KRRajGu5KhRo3jxxRdDKhyef/55SktLeeCBByrdvmPHDp599llee+21oG0sW7aM5cuXq8sdOnRg7ty5NQ88jI5m7qb5a10BcD1Qis4QeJrl4J8/0XZZPwxuKHu06Q71KYQQIrI0yFwP69evZ/v27TzzzDNB94mPj8dms+FyudDr9ZXuM3z4cIYNG6Yu+6u0vLw83O7G3WV/cNcfAMQ6Ie+ob5hmjUZDamoq2dnZOLy+nJ06SN+zk5i4+LDFeiKOz6kpj21emUjMLRJz8ovE3CIxJ79IzK2x56TT6UhOTq5+v/oOZO/evbz++uvcd999JCQkqOv//e9/c9lll3HKKacAsGvXLuLj44MWCQB6vT7o9sb4JhzPXuSbPjrepa0Qq6IoGC3N0SigaMB+9DDRsRVHb2xKFEVp9O9JbUVibpGYk18k5haJOflFYm5NPac6GXCppKSk0m/0RUVFzJ07lyuuuIJOnTrhcDhwOHy3/7Vt25Y333yTnTt38vPPP7NkyRIGDx5cF+HUK8Xr5b3FU/h25XMhHWez+W4pNbsrvwVUG6XDXH7Dg60w+4RiFEIIIepKnfQo3HvvvYwfP55zzjknYP3GjRspLCxk6dKlLF26VF2/bNkyrrzySnJzc3n88ccxGo0MGTKE4cOH10U49erXr19nmucTmqVr+IO7a3yc3Z4PQLw3eI9JvCsKa7RH7X0QQgghwq1WhcKyZcsCll966aVK9xs6dChDhw6t/Il1OqZMmcKUKVNqE0LY/G/L2xAPR40KJYW5xCbU7IJOe4mvUDB7DUH3sbh1gAe7/UhdhCqEEEKcMJnrIQTOUjsrDHvU5bzMP2t8rK20EACLJvigUpby3gZbcX7tAhRCCCHqmBQKIfh+zULyjccuSMnN3l3jY23l00ebtcag+1iI9u1bUlDLCIUQQoi6JYVCCFb8tTxgOe/I/hofa3fZALBExQXdx1ze22BzSKEghBCicZBCoYZKrUf4LC4TgK5FvlMEOUWZNT7e6rIDYNIHLxQs2ljgWO+DEEIIEW5SKNTQ158/T4kBOll1DInqDkBOSU6Nj7d5fdNHmw3Bx0ew6HzzXFjLex+EEEKIcGuQkRkjwccHVkISjDD0wVL+YZ/tqvlFhzalvFCICT7iotlgBsDuLjmBSIUQQoi6Iz0KNVCQtY818b5bFi8//xZaJLQBIFup+SkCK2UAmGMTg+5jivYVIFavFApCCCEaBykUamDNF8/hjoLTC2LoePrFpDRvD0BOVGmN27BqfMMummKTgu5jiUnw7Yuj1rEKIYQQdUkKhRr4KHctACPizwMgJdU3C+RhoyvoMX9n0/mGuDaZmwXdxxTnKyL8RYUQQggRblIoVCNrz2Y2JNnQKDD04qkAJLfxXcxoN0BxQc3mZSjSeQEwmYPP1GU2+YqIIm3NCxAhhBCiPkmhUI1VX80HYECBmdROZwBgtDTH4rvkgNyDO6ptQ/F6KYr2DdRkim8RdD9zeRFh03lOJGQhhBCizkihUI0PizYAcHXKxQHr0xy+sRTyajA6o6O4EE/5K21OSgu6n7+IKNJ7axOqEEIIUeekUKjCvt/W8luiA50HBl8aOFNkmts3OFLe0QPVtmPPPwyA1uvrjQgmLt43wVSJAVxlcueDEEKI8JNCoQorv3sZgMFFzUlM6xiwrYXWdytjjvVQte34p422OEGjDf6Sm5JSjx2TX7NrH4QQQoj6JIVCEIrXy4fOXwEY3u7yCttTDb4LD7NLqv9AVwsFV1SV++mjY4ktv+GhuFAKBSGEEOEnhUIQ2zYuY6/FTawTLho6tcL2FnG+6wlynNWPzmi3HwXA4ql+IMx4l+8tsVlzQ4hWCCGEqB9SKASxctPrAPyzuHWl1xW0iC8fnZHq52WwFfsKBbNXX+2+Frev18FuO1LjWIUQQoj6IoVCJTwuJx9qfbc9XtXtmkr3SS4fnTFbV/3ojPbSIgDilehq97WUFxO28l4IIYQQIpykUKjEpq9eJTvOS1Kphv6Dp1S6T0pqFwAOx7hRvFXfzmhzFABg0sZU+9wWxQCAvbjmE04JIYQQ9UUKhUr8b+sSAK5ydsZgNFW6T/M2PQDfrYz2/Kwq27OX+U5PWLSx1T63BV8xYXUU1jRcIYQQot5IofA3juIi/heTDsCVZ4wLup/RnERC+dxNRw7trLJNq8tXKJh1cdU+v7+YsDmKahKuEEIIUa+kUPibDatfpCgGWtu1nHnh+Cr3TS3znSbIrWZ0RrvHN3iSWW+u9vn9xYTdZa9JuEIIIUS9kkLhb/63+2MArtb0QhtV9e2MaW4jALlH91e5n9VfKERbqn1+s953qsPqlkJBCCFE+EmhcBx7fhafm33XG1xx7qRq90/VxgPVj85ow3eOwmyMr7ZNfzFh9dZsCOfMnT/y9qs38dfPn1R7UaUQQggRqupHADqJfLXqORx66Fak55Szr6h2/9To5kAGuSVVD45kxTfcosmYWG2b5phEcIBNcdQo5if+dwv/S8qB3z+nxzd3ck3cuVx+yV207HJ2jY4XQgghqiI9Csfpfdo/mVp8BjclXVblnAx+KbG+SZyyXVXfymjVugAwm5pV26Y51ldMWKlZobAjyjfegkaBHQlOHtF/w9nrr2LU3FNY+vptFObsr1E7QgghRGWkR+E47XtdyL29Lqzx/i0S2kAhZGGtcj+rzg2AyRx85kg/c1wS5INV6652X7fTQbrJt9+6M19h644v+TBrNd8mWtmYZGOj+2P0H3/M4KJkRnS8iouvepAovaH6xIQQQohy0qNwAlKa+2aUzNZV/e3fqvddO2CypFTbptmc7DtGV32hkLXnF9xREO2GzmcMYcQNz7Pk/j/5ZdCnPOIZxGkF0bii4LOkPP6vcBE3P3sWpTYZyEkIIUTNSaFwApLTugJw2Bh8dEaPy4mtfORmU0L1hUKcxVcoFOk91e6bkb4ZgI52Q8AdGqmdzmDi5LdYdd8+1p/5KneVnEmMC1Y1O8q4l8/Fmnew2raFEEIIkELhhDRv0x0Ahx5sRyu/86GkKE/9OS4xrdo2zQmpANgM4PVU3auwP9s3H0VHb/C7Kbr0uYxpd67kg1MeJ94BPyTZGfXGP8g7sK3aWIQQQoiQCwWr1cqtt95Kbm7NpkHesWMHd911FxMnTuTTTz8N2Pbjjz9yyy23cPPNN7Nhw4ZQQwm7mLh4Eh0aAPIy/6x0H3thNgAGN0THVj+OQlySr5hQNFBckFPlvgeKfCNIdjRUX4CcOXACH/f7L6nFWrYmlHH18n9ycEfTe82FEEI0rJAKBavVyty5c8nLy6t+5+P2HzBgAI899hjfffcd27b5vslmZGSwYMECRowYwUMPPcSyZcs4fPhw6BmEWZrDN9tjsNEZ7UW+gireqalRezFx8RjKOxL8RUYw6S7f9vYJHWvUdrezh7Fi8Ht0tOrYZ3Fz5ZfX8tfPn9ToWCGEECenkAqF+fPnM2DAgBrv/91335GUlMSIESNIS0vjmmuuYd26dQCsW7eOnj17MmjQINq2bcull17Kt99+G1r0jUCa1zc3Q27+/kq3262+osrijqpxm/6iwl9kBLM3yjcfRLvUHjVuu02P8/joms/oVRhNTqzC8J+msHndGzU+XgghxMklpNsjb775ZlJSUnjjjTdqtP+BAwfo2bMnGo3vg69z584sWbJE3Xb66aer+3bu3Jnly5dX2Z7L5cLlcqnLGo0Go9Go/hwOLbTxQCG51qxKYyguny7a4tFV2O5f/vt6izuKPNzYrXlB8/K4nKTH+V6Ldp3ODin/lPa9+ODGb5n42iA2JtkZufMhFtuPcOGV99a4jWCC5RQJIjG3SMzJLxJzi8Sc/CIxt0jJKaRCISWl+qv2j1dSUkLr1q3VZaPRSH5+vrrt+PaMRiMFBQVVtvfxxx8HFBMdOnRg7ty5JCcnhxRXXWprSgMOkO/OJy2t4rUC3vKBk+KJqXQ7QGpqasByvFcPuNFoyoIec2D7Rpw60HvgjAFDQx4fIS0tjTWPpDNqdk8+S8hlXNa/WfyRnRtufSWkdoL5e06RJBJzi8Sc/CIxt0jMyS8Sc2vqOdXrgEtRUVHodMeewmAw4HQ61W16vV7dptfrKSsrq7K94cOHM2zYMHXZX6Xl5eXhdlc/7kB9SNQ3AwUOluSSlZVVYXt2+d0QJsVQYbtGoyE1NZXs7GwURVHXm70GoJTD2QcqbRNg889fANDRrif3yNFax//ynd8z/fmBvJeYwfgjizAtTuHcy26tdXvBcooEkZhbJObkF4m5RWJOfpGYW2PPSafT1eiLdr0WCiaTCav12KiFpaWlauHw920OhyOgqKiMXq8PKC6OF643ISWhNRRANtZKY7CVFkEUmDUxQWNUFCVgmwXfwAvW0oKgx+w/vB2Ajm7LCeWuMxh5+p7vUJ49j/cTD/LG74vod+kttW7P7+85RZJIzC0Sc/KLxNwiMSe/SMytqedUr+ModOrUid27j90NkJ6eTlJSkrpt165dlW5rSlKSy0dn1Fc+OqPNaQPAEhVX4zYtGt91F/ay4END7y/aB0AHw4l3aWmjdEweNAuAVfG55B+u/A4OIYQQJ586KRRKSkoq7fo/66yz2LlzJ3/88Qdut5tPPvmE3r17A9C3b182btxIRkYGDoeDVatWqduakuTULgAcNnoqHZ3R5rYDYNbVvFDw72urolBIL/OdkmgX36HG7ValS5/L6FNgxB0Fn372VJ20KYQQoumrk0Lh3nvvZfPmzRXWWywWxo8fz5NPPslNN93E4cOHufrqqwFo3749Q4cOZfr06dx8881otVqGDBlSF+E0KP/ojGU6KMrLqLDd5i4GwBxd/WBLfmadCQBreZFRmX1a362R7Vt0r3G71Rnd7GIAlhasr7M2hRBCNG21ukZh2bJlAcsvvfRS0H0HDx7M6aefzqFDh+jevTsxMTHqtjFjxnD++eeTn59Pjx49qr1GoTGKjrXQrFTDUaNCXuafJLRoH7DdqpQCYI4OPszy31nKiwqrp7jS7V6Pm31xvgs/23boU4uoKzd06H3MWLGS3xId/PXzJ3Q754o6a1sIIUTT1CBzPaSkpHDGGWcEFAl+rVu35rTTTmuSRYJfWpnv1sQjuXsrbLMpvg90szGhxu2ZY+LLjy2tdHvege2U6iHKC2md665QSEzryFCr75qHDzcGL/6EEEKcPGRSqDqQWj46Y87R/RW2WbW+20FNcTW/UNNsTASgiMpvF83Y+zMA7e069NGxoYRarZGnXAvAMu12XGUlddq2EEKIpkcKhTqQqvX1AORYK85VYdP6Rk8MqVCIa+Y7VuOqdPuBw775Mjq6zSHFWRMDLr2VFiUa8mIVvlv1Qp23L4QQommRQqEOpBqaA5BTWnFuhiK9BwCTpeajR8aZfEWFVVd5obC/sPzWSF2LkOKsCX10LKO8vQD44K+ldd6+EEKIpkUKhTqQYvINs5ztzq+wrcjgG2TDFF/z4a/NFt++RbqKt1sCpDt8PRftLe1CirOmrj7/NgBWxedQkFXxugshhBAnDykU6kBKgm8+ixwCb2csK7FSVn6Npimh5gMjmeJ9PQVF0UqlYzPs0/jmxGjfouazRoai61n/5MwCI64o+OyzufXyHEIIIZoGKRTqQEqyb9CjrL+NzlhcmKP+HJdQ89ME5iRfD4VHCw57YC+F4vWy139rZPszaxVvTYxOGgjA+/lf1dtzCCGEaPykUKgDKS1PAeBwbODojP5CweQkpNkdjZbmaMubsRdkB2w7mrmTYgNovdCy81knGHlw//zn/eg9sCXRwV+bPq235xFCCNG4SaFQB5q16gaAKwoKc9LV9Xar7+JGiyu0l1mj1WJxlrdxXK8EQMYe362RbYujiI6t+WiPoUpM68TQIl8vyEcbXqy35xFCCNG4SaFQBwxGE81Ly6e8PrRTXW+3+aZ/trhDH0wq3hUFgM0aeCfFgUNbAejorPtbI/9uZDffmAofaLfJmApCCHGSkkKhjvhHZ8zLOXaXgM1eXih4Qi8U/MfYbUcC1u8v8LXfQVfzuyhq67zLbiOlRENOrMLGL2SkRiGEOBlJoVBH0ry+GR9z8g+o6+wlvrsTLESH3J7F6ys8bMVHA9bvLz0EQHtz21rFGQp9dCwjvT0B+GDn+zU6RvF6OZop01QLIUSkkEKhjhwbnfGQus7qKATAXItCIb78GFt5seGn3hqZUnezRlZlxHm3A/BZfDYFWfuq3Ddrz2ZGPd2d5Fe78sMq6YEQQohIIIVCHUmJ9o3OmOvIU9fZy6wAWLShz8dg0vgm0LI5itR1itfLXqNvoqi27U6vbagh6Xb2ME4viPGNqfB58DEV1q2Yy+DPr2Bjkg1FA9v2/9Ag8QkhhKhfUijUkRZxvgGVst3HegBsThsAJl3ohYK/uLA5req6wpx0ison4GzVpW9tQw3ZteVjKiw9WnFMhbISK48/N5hxeQvINypEld/WaS0rqrCvEEKIpkcKhTrSItF3zUA2NnWdzV0MgEUf+h0KFr3J14br2GiPGbt/BKC1XYvRnFjrWEM17J/T0Xtgc2Ipu39dpa4/sO0bRrx4Bi+btwNwq/1Upjp8014Xum2VtiWEEKJpkUKhjqQkdwQgy3Bsamir13dLockQeqFgNvjGSLB6itV1GZn+WyNNtY6zNhLTOnGZf0yF73wzSn723oMM/uY6tiQ6aFaq4d2UqTx412qaxSZXiFsIIUTTJYVCHWme1hWAbKMHr8cNgN3ru57AbEwIuT1zdHmhUN4GQPrRXQB0jKr5TJR1ZWTXUQAs1fzB9GfOY5L9TewGOC/fzJp/ruTCK+8FICHWN/NlkVIatC0hhBBNhxQKdaR5G98wzsePzlik8fUumGMSQm7PYvSdWrApx+aPSPffGhlX/7dG/t35Q+8guXxMhbcT0tF6YXpZP5bc/Qepnc5Q9zPHNgOgEEewpoQQQjQhUijUEX10LCkl5aMzZv4JgE3rAsBsahZye3GxvkLBqnWq69LxTRDVLrnrCcVaG/roWK5TegPQsljLRx1nc/ttH1aYw8Ji9vV2FEW5GjpEIYQQ9SD0IQNFUGll0eTGOsjN2Us3wBrlOwURV4tCwWxuDrlg1R77wN0b4+vOb9f29LoIN2RTb17K6Z89x9nDxpKY1rHSfSzl02kX6t0NGZoQQoh6IoVCHUpV4vgdB7kFvtEZrXrfvYJmS+jDLZv838z1HgAKc/aTb1QAaNO1X12EGzKD0cTgax6uch9zeaFQYFBQvF40Wum0EkKIpkz+F69D/tEZc21ZKF4vVoPvgz0uPvRCwZzgu8ugqLyNzN2+WSPTirXEJtT/PA+1ZWnWCgCnDhzFMpaCEEI0dVIo1KEWMb5egOzSXBz2fDzlr645KS3ktkzl38zLdL5BjTIy/wCgY1lc3QRbT+ISWqAtH3TJnn+o6p2FEEI0elIo1KEWJl9BkO0pxJafBUCUF2JMSSG3FVfeowBQXJjD/iPlt0Zqm9dBpPVHo9WSUOa7qNOafzjM0QghhDhRUijUoZSENgBka+wUF+UCYHFqanWePkpvwFR+w4O9IJv0kkwA2se1qZtg61GCOwoAa1FOmCMRQghxoqRQqEP+0Rmz9Q5sVl+hEO+q/Usc7/Qda7fmkq4cAaB984a/NTJUiZ7yKbJtedXsKYQQorGTQqEOJbfsBkBWrBeb1fchafbU/sYSi9t3rM2ax94Y33DQ7dr2PsEo61+C4psi22qXQkEIIZo6KRTqULPW3dAo4NHC/uwdAFi8hmqOCi7eqwcg+8hecmN9dz+07txws0bWVkL5zJdFJQXV7CmEEKKxk0KhDukMMbQo8b2kewv3AGBRal8omPEduy3ndwCSSzSYm7c6wSjrX2KUb9Iqq6MwvIEIIYQ4YSH3i2dkZLBw4UKys7MZOHAgY8eORaPRBN3/pZde4ptvvqmw/sUXXyQ5OZkbb7yRkpISdf3o0aMZMWJEqGE1GqlOA9lxDna7fHc9mLXGWrdlwQgU8JvLN4BTJ0dsXYRY7xLKZ8sscso4CkII0dSFVCi4XC7mzp1L7969ufPOO3n99ddZv349F110UdBj/vWvfzFhwgR1edeuXbzxxhs0b96crKws4uLieOmll9TtBkPtv4E3BmneOH7Dwa5oKwBmbe0/3M1RvmN/N9kB6KAJfSjocEiISQAvWN32cIcihBDiBIV06mHLli2UlJQwfvx4UlNTGTNmDOvWravymOjoaOLi4tTHZ599xsiRI9Fqtezdu5cuXboEbNfr9SeUULi10Pkmc8o0+UYdsuhNtW7LovMNrlRcXjt1iG19YsE1kMTyGSSLvCXV7CmEEKKxC6lH4cCBA3Tt2pXoaN9V7e3atSMzM7PGx+/Zs4fc3Fz69++vLu/du5cJEyag0+m4+OKLGT16dNBTGS6XC5fr2CRJGo0Go9Go/twYpMY0B/aoyya9KWhs/vXBtlv05oDl9s26NJo8g9FoNCTENQM7FFLa6OMNRXXvV1MUiTn5RWJukZiTXyTmFik5hVQolJaWkpycrC5rNBq0Wi12ux2Tqfpvzl988QWDBw9GWz4AUVZWFn369GHo0KFkZ2czf/582rRpw4ABAyo9/uOPP2b58uXqcocOHZg7d25ATOHWPrkjWH9Ul1sktCAtreohnFNTUytdn5LQAo77Ut671z+qbasxSLCkQI5viuymEG+ogr1fTVkk5uQXiblFYk5+kZhbU88ppEJBq9VWODVgMBhwOp3VHmu329m0aVPA9QoPPvig+nNKSgqXXXYZP/74Y9BCYfjw4QwbNkxd9ldpeXl5uN2NY1pjc0wyWI8tRxFNVlZWpftqNBpSU1PJzs5GUZQK23VEB7ad0i1oW42FRqMhMdFXHBRqXY0+3lBU9341RZGYk18k5haJOflFYm6NPSedTlejL9ohFQomk4mDBw8GrCstLUWnq76Zn376ie7du1fZ82CxWMjPzw+6Xa/XB72GobG8CSkpnSD32LIpNqna2BRFqXQfkzFR7VFIdGiIT2nfaPKsSkJSSwAKDJ4mEW+ogr1fTVkk5uQXiblFYk5+kZhbU88ppIsZO3fuzK5du9Tl3NxcXC5XjU47/PDDD5xzzjnqstPp5J577gnojdi1a1ejOo1QG/7RGf3McaFPCKUeazp2l0On0trfZtnQEpr7LrosMih4PY2jp0cIIUTthFQodO/endLSUr7++msAPvroI3r16oVWq6W4uBiv11vpcU6nkx07dtCzZ091ncFgICEhgVdffZW9e/fy6aefsnHjRgYPHnwC6YRfUsuu6jTLACZL7Qsf83HHdqD2BUdDS2zRHgCvFkqKZBhnIYRoykIqFKKiopg8eTKLFy9m4sSJ/PLLL4wdOxaAG2+8kYyMjEqP++uvv4iLi6NFixYB66dMmUJeXh4PP/wwa9eu5c4776RHjx61TKVxiNIbaFF67GU1mWs/LbTJkqL+3NHY+Edk9IsxJRBd3pFgPVrzu2KEEEI0PiGPzHjWWWfxwgsvsG/fPrp06YLZ7LuFb9myZUGP6dWrF4sWLaqwvnnz5syaNSvUEBq9NGc0WXGlAJgSa3/Vf1zCscKqfVKXE46rISU4NeToFGwFkXMxoxBCnIxqNbVhQkICZ555Zl3HEjHSlDjAVygc/2EfquOLjLate51oWA0q0aUjBxdWa271OwshhGi0ZFKoepASlQBAjAsMxtqPzGgwmuho1WFyQqeewYfJboziPb7hJKVQEEKIpq1WPQqiaqkxycAeLK4TH43rf2PW4igubBKzRh4vnmigGGvx0XCHIoQQ4gRIoVAPWphbghPiXVEn3FZSy6Z1bYJfgiYWyMfqKAh3KEIIIU6AnHqoB21anAJAiqfpjH1Q1yxRvgmtihyF4Q1ECCHECZEehXpwziWTmPvG75x90T/DHUrYxOt812ZYXbYwRyKEEOJESKFQD7RROsZO/G+4wwir+OgEAIrcxeENRAghxAmRUw+iXliMiQAUKVIoCCFEUyaFgqgXlljfkNOFlIU5EiGEECdCCgVRL+LNvnkqirTVT0EuhBCi8ZJCQdQLS/k8FYU6V5gjEUIIcSKkUBD1wpyQCkChofIZRYUQQjQNUiiIeuGfp8JuALfTEeZohBBC1JYUCqJeWJq3Vn+2HT0UxkiEEEKcCCkURL3QGWIwlV/HaMs/HN5ghBBC1JoUCqLeJDh9v162wuwwRyKEEKK2pFAQ9SbBrQfAas0JcyRCCCFqSwoFUW/ivQYArPYjYY5ECCFEbUmhIOpNAtEAWEvywxyJEEKI2pJCQdSbeE35VNOlBWGORAghRG1JoSDqTbzOVyhYy4rCHIkQQojakkJB1BuL3gxAkcsW5kiEEELUlhQKot7ExyQAUOiVqaaFEKKpkkJB1BtLTCIAVqU0zJEIIYSoLSkURL2xxDUDoIiyMEcihBCitqRQEPXGP9V0QZQzzJEIIYSoLSkURL3xFwpFeneYIxFCCFFbUiiIemMun2q60KCEORIhhBC1JYWCqDeWZr6ppst0UGqTQZeEEKIpkkJB1JvY+GS0Xt/P9vxD4Q1GCCFErehCPSAjI4OFCxeSnZ3NwIEDGTt2LBqNpspjpk2bRkZGhro8cOBAJk+eDMCPP/7IW2+9hcfjYdy4cZx33nmhhiQaKW2UjninhoIYBVtBNsntTg13SEIIIUIUUqHgcrmYO3cuvXv35s477+T1119n/fr1XHTRRUGPKSsrIycnh1dffZWoqCgA9Hrf9MMZGRksWLCAiRMn0qVLF+bNm0fHjh1p2bLlCaQkGpNEZxQFMW6KCrPCHYoQQohaCOnUw5YtWygpKWH8+PGkpqYyZswY1q1bV+Ux6enptG3bFovFQlxcHHFxcRgMvumH161bR8+ePRk0aBBt27bl0ksv5dtvv619NqLRiff4alGbLS/MkQghhKiNkHoUDhw4QNeuXYmO9k0f3K5dOzIzM6s8Zs+ePeTn5zNx4kQ8Hg8DBgxgwoQJ6PV6Dhw4wOmnn67u27lzZ5YvXx60LZfLhcvlUpc1Gg1Go1H9uanxx9wUYw/m7znFK9GAA5v9SJPP82R4vyJJJOYWiTn5RWJukZJTSIVCaWkpycnJ6rJGo0Gr1WK32zGZTJUec/jwYbp168aoUaMoLi5mwYIFfPbZZ1x11VWUlJSQkpKi7ms0GikoCH51/McffxxQSHTo0IG5c+cGxNQUpaamhjuEOufPqVmUCSjCqZSQlpYW3qDqSCS/X5EoEnOLxJz8IjG3pp5TSIWCVqtVry/wMxgMOJ3BR96bNGlSwPI111zDqlWruOqqq4iKigpoT6/XU1YWfLjf4cOHM2zYMHXZX6Xl5eXhdje9QX00Gg2pqalkZ2ejKJEx1sDfczIpvt6nnMJssrKa9nUKJ8P7FUkiMbdIzMkvEnNr7DnpdLoafdEOqVAwmUwcPHgwYF1paSk6Xc2biY+PJz8/X23ParWq2xwOR5Vt6fX6CoWKX2N8E2pKUZQmHX9l/DlZdL6epiKnNWJyjOT3KxJFYm6RmJNfJObW1HMK6WLGzp07s2vXLnU5NzcXl8sV9LQDwEMPPcSRI0fU5V27dqkVTKdOnQLaS09PJykpKZSQRCMXb4gHoMhlC3MkQgghaiOkQqF79+6Ulpby9ddfA/DRRx/Rq1cvtFotxcXFeL3eCse0adOGRYsWsXv3btavX8/KlSu55JJLAOjbty8bN24kIyMDh8PBqlWr6N27dx2kJRoLS0wCAEXekvAGIoQQolZCKhSioqKYPHkyixcvZuLEifzyyy+MHTsWgBtvvDFgUCW/cePGodPpmDNnDh988AHjxo3jwgsvBKB9+/YMHTqU6dOnc/PNN6PVahkyZMiJZyUajfjYRACKlNIwRyKEEKI2Qh6Z8ayzzuKFF15g3759dOnSBbPZDMCyZcsq3T8uLo577703aHtjxozh/PPPJz8/nx49eoR0vYNo/CymZLBDoTb4RapCCCEar1p9KickJHDmmWfWWRCtW7emdevWddaeaDzM5mTIhqKopndXihBCCJkUStQzS3wLAAr1njBHIoQQojakUBD1ypLkm7ejMFrB65FeBSGEaGqkUBD1ypTUCgCvFkqKZL4HIYRoaqRQEPUqJi4eQ3lHgi3/cHiDEUIIETIpFES90mi1JDp9Q21b8w+FORohhBChkkJB1Lt4l+/mGqs1N8yRCCGECJUUCqLeJXh883PYbEeq2VMIIURjI4WCqHcJxABQZJeLGYUQoqmRQkHUO4vGCIDVURDmSIQQQoRKCgVR7+K1cQBYy4rCHIkQQohQSaEg6l2C3jcfSJHTGuZIhBBChEoKBVHvLNHxABS5i8MciRBCiFBJoSDqnSUmAYAipSS8gQghhAiZFAqi3sXHNQOgEEeYIxFCCBEqKRREvbOYmgNQpHWGORIhhBChkkJB1DuzOQWAIp3MHimEEE2NFAqi3lkS0wAoMHjCHIkQQohQSaEg6p05qSUAdgO4nXKdghBCNCVSKIh6Z0pKU3+2HZWppoUQoimRQkHUO310LKby6xhtMtW0EEI0KVIoiAaR4PT9qtkKs8MciRBCiFBIoSAaRLzbN9W01Zob5kiEEEKEQgoF0SASvAYArDLVtBBCNClSKIgGkUA0ANaS/DBHIoQQIhRSKIgGYdHEAmAtLQxvIEIIIUIihYJoEAk6EwBFZYXhDUQIIURIpFAQDcKiNwNQ5LKFORIhhBChkEJBNAhLdDwARV6ZaloIIZoSKRREg4g3JgFQpEihIIQQTYku1AMyMjJYuHAh2dnZDBw4kLFjx6LRaKo85oMPPuDzzz+nrKyMM844g9tuuw2j0QjAtGnTyMjIUPcdOHAgkydPDjUs0chZ4pqBA4ooC3coQgghQhBSoeByuZg7dy69e/fmzjvv5PXXX2f9+vVcdNFFQY/57rvv2LBhAw899BAmk4mnnnqKFStWMGbMGMrKysjJyeHVV18lKioKAL1ef2IZiUbJYk6Go1AY5Qx3KEIIIUIQ0qmHLVu2UFJSwvjx40lNTWXMmDGsW7euymOOHj3KrbfeSufOnUlNTaV///7s378fgPT0dNq2bYvFYiEuLo64uDgMBkOtkxGNlyW+BQCFeneYIxFCCBGKkHoUDhw4QNeuXYmO9g2e065dOzIzM6s85qqrrgpYPnz4MKmpqQDs2bOH/Px8Jk6ciMfjYcCAAUyYMCFor4LL5cLlcqnLGo1GPYVR3emPxsgfc1OMPZhgOVnKp5ouNChNNt+T6f2KBJGYWyTm5BeJuUVKTiEVCqWlpSQnJ6vLGo0GrVaL3W7HZDJVe/zhw4f5+eefmTt3rrrcrVs3Ro0aRXFxMQsWLOCzzz6rUFz4ffzxxyxfvlxd7tChA3Pnzg2IqSnyF06R5O85xUadDl9DmQ4SzUZiTAlhiasunAzvVySJxNwiMSe/SMytqecUUqGg1WorfNs3GAw4ndWfd/Z6vSxcuJCBAwfSpk0bACZNmhSwzzXXXMOqVauCFgrDhw9n2LBh6rK/SsvLy8Ptbnpd2hqNhtTUVLKzs1EUJdzh1IlgOXk9GrRe8Gph17ZNJLc7NYxR1s7J9H5FgkjMLRJz8ovE3Bp7TjqdrkZftEMqFEwmEwcPHgxYV1paik5XfTMffvghdrudcePGBd0nPj6e/PzgcwHo9fqgpyUa45tQU4qiNOn4K/P3nDTaKOKdGgpiFKz5WTRv2zOM0Z2Yk+H9iiSRmFsk5uQXibk19ZxCupixc+fO7Nq1S13Ozc3F5XJVe9rhl19+4dNPP+Wee+5Rr28AeOihhzhy5Ii6vGvXriZ/GkEEl+Dy/bpZi7LDHIkQQoiaCqlQ6N69O6WlpXz99dcAfPTRR/Tq1QutVktxcTFer7fCMZmZmcyfP5//+7//o3nz5jgcDsrKfPfSt2nThkWLFrF7927Wr1/PypUrueSSS+ogLdEYJbh9vUFWa26YIxFCCFFTIRUKUVFRTJ48mcWLFzNx4kR++eUXxo4dC8CNN94YMHCS39q1aykrK+Oll17ihhtu4IYbbuDuu+8GYNy4ceh0OubMmcMHH3zAuHHjuPDCC088K9EoxXt9vUk2+5Fq9hRCCNFYhDwy41lnncULL7zAvn376NKlC2azb7KfZcuWVbr/hAkTmDBhQqXb4uLiuPfee0MNQTRR8ZoYoIiikuDXoQghhGhcQi4UABISEjjzzDPrOhYR4RK0cQAUOQrDG4gQQogak0mhRIOx6HwXvVpd1jBHIoQQoqakUBANJt5gAcDqtoc5EiGEEDUlhYJoMJaYRAAKPcVhjkQIIURNSaEgGkx8rK9QKFJKwxyJEEKImpJCQTQYc1wzAIq0MtW0EEI0FVIoiAZjsZRPNR3lqmZPIYQQjYUUCqLBWOLLCwW9J8yRCCGEqCkpFESDMSemAVAYraBUMty3EEKIxkcKBdFgzM1aAb6pposLc8IcjRBCiJqQQkE0mJi4BAxu38+2/MPhDUYIIUSNSKEgGoxGqyXBqQHAWpAV5miEEELUhBQKokEluHzTi1iLssMciRBCiJqQQkE0qASPHgCbTaaaFkKIpkAKBdGg4okGwFoshYIQQjQFUiiIBhWPEYCi0vwwRyKEEKImpFAQDSo+qnyq6bKiMEcihBCiJqRQEA0qXl9eKDhtYY5ECCFETUihIBqUxRAPQKHbHuZIhBBC1IQUCqJBxRv9U02XhDkSIYQQNSGFgmhQltgkAIpwhDkSIYQQNSGFgmhQFlNzAAq1zjBHIoQQoiakUBANymLxTTVdpHOHORIhhBA1IYWCaFDmBF+hUGDwhDkSIYQQNSGFgmhQ5qSWANgN4HbKdQpCCNHYSaEgGpS5WSv1Z9tRmWpaCCEaOykURIPSR8cSV34doy3/UHiDEUIIUS0pFESDS3D6fu1shTLVtBBCNHZSKIgGl+D2TTVtteaGORIhhBDVkUJBNLh4b3mhYM8LcyRCCCGqowv1gIyMDBYuXEh2djYDBw5k7NixaDSaKo/58ccfeeutt/B4PIwbN47zzjtP3fbFF1/w4YcfEh0dzeTJkzn11FNDz0I0KQnEAHasJTLVtBBCNHYh9Si4XC7mzp1Lhw4dePLJJ8nMzGT9+vVVHpORkcGCBQsYMWIEDz30EMuWLePwYd/V7r/99htvv/02kyZN4vbbb+c///kPNpvMKhjp4jWxAFhLC8MbiBBCiGqF1KOwZcsWSkpKGD9+PNHR0YwZM4bXXnuNiy66KOgx69ato2fPngwaNAiASy+9lG+//ZZrr72WNWvW8I9//IOzzz4bgLPOOouff/5Z3ffvXC4XLpdLXdZoNBiNRvXnpsYfc1OMPZia5BSv8001/aF1I3ueOS/ofo2PBp0uCrfbAyjhDqaORGJOfpGYWyTm5Fd3uc3KO41UjzHk4w7Ym7M+91S+zenB4dIkEgzFJOiLiTeUkGgoJsFgJ15fQoKhmERDMYkGO2nGAsz6IGPCaDRgNBJfWgpKYE5Wl5GM4uYcLG7OkTIzRp2TuKgy3786B8YoJ3G6MoxRZcTpyoiNKsM15GLKLrusNi/JCQmpUDhw4ABdu3YlOjoagHbt2pGZmVntMaeffrq63LlzZ5YvX65uO/40ROfOnfnzzz+DFgoff/yxeixAhw4dmDt3LsnJyaGk0eikpqaGO4Q6V1VOnZp1AOcOfk908DvpDRiVEOJk8NDb6cQerX6/w6TxNRexjoGsYyD76VCr54vDTksOq480stSf9RRxgHYVHoUkhvw8M7K+4dH/S6tVjCcipEKhtLQ04ENZo9Gg1Wqx2+2YTKZKjykpKSElJUVdNhqNFBQUqO0dvy02NlbdVpnhw4czbNiwgOcHyMvLw+1uenMHaDQaUlNTyc7ORlEi49tBTXK66spH0XwUhbWsqIGjOzEaIDo6mrKysoj5LheJOflFYm6RmJNfXeZmmHQGVir2KCgK/O+vU/l2f0e+PdCJ3fmBXzJ1Wg990jK5oN1eujbLw+qModARQ5HDSKHDSFFZDIX+nx0x5JfGYS2LoRgTu+nKbrqGFGdiTAlt4gtIji2mzKOjxKWnxGVQ/y116Slx6VHKrxLwtEkmKyur9i/M3+h0uhp90Q6pUNBqtej1+oB1BoMBpzP4TIBRUVEBx+j1esrKyqrdVhm9Xl/h+f2a8getoihNOv7KVJVTbEILxvzfwgaO6MRpNBrS0tLIysqKmPcrEnPyi8TcIjEnv7rOzR5k/RMDk/nrL9/niFar0KuXiwEDyujf38k55ziJi9MDp5Q/qlIGlFFSoiEnR0tOThQ5OVqys6PUn3NyotBooklOLqV1azetWnlo3frYIy5OwVciHf9F21P+8J3OUBRwOKC0VIvBkBiW9z2kQsFkMnHw4MGAdaWlpeh0wZsxmUxYrVZ12eFwqPv/fVt1bQkhhBAnYuTIErKyojjvvDL69nUSH39iH7yxsQodOnjo0KHiRHfHip/CWn/Al1/mgNHoPaE4T0RIn8qdO3fmq6++Updzc3NxuVxBTzsAdOrUiV27djFw4EAA0tPTSUpKCtjWq1cvAPbv369uE0IIIeralCnF4Q6hyQnp9sju3btTWlrK119/DcBHH31Er1690Gq1FBcX4/VWrHj69u3Lxo0bycjIwOFwsGrVKnr37g1Av379WLNmDfn5+RQWFrJu3Tp1mxBCCCHCL6QehaioKCZPnsz8+fN555130Gg0zJ49G4Abb7yRp59+mvbt2wcc0759e4YOHcr06dPR6/WkpaUxZMgQAPr06cMPP/zAHXfcAcCpp55K3759TzwrIYQQQtQJjVKLEyeFhYXs27ePLl26YDaba3RMZmYm+fn59OjRo8J1CHv27KGsrIwePXrUakyBvLy8gPEVmopIvDApEnPyi8TcIjEnv0jMLRJz8ovE3Bp7Tnq9vu7vevBLSEjgzDPPDOmY1q1b07p160q3de7cuTZhCCGEEKKeyaRQQgghhAhKCgUhhBBCBCWFghBCCCGCkkJBCCGEEEFJoSCEEEKIoKRQEEIIIURQETGxQlOfH6Kpx1+ZSMzJLxJzi8Sc/CIxt0jMyS8Sc2usOdU0rloNuCSEEEKIk4Ocegij0tJS7r//fkpLS8MdSp2JxJz8IjG3SMzJLxJzi8Sc/CIxt0jJSQqFMFIUhfT09EY5tGdtRWJOfpGYWyTm5BeJuUViTn6RmFuk5CSFghBCCCGCkkJBCCGEEEFJoRBGer2ea665Br1eH+5Q6kwk5uQXiblFYk5+kZhbJObkF4m5RUpOcteDEEIIIYKSHgUhhBBCBCWFghBCCCGCkkJBCCGEEEFJoSCEEEKIoBrnANSNzKZNm3jzzTc5cuQIbdq04c4776R169ZkZGSwcOFCsrOzGThwIGPHjkWj0ajHZWdn88ADD/D6668HtLd8+XJWr16Nw+Hg1FNPZcqUKVgslmrj2LFjB4sWLcJqtTJ8+HCGDRsWsP2vv/7i5ZdfZv78+U0+p5deeolvvvmmwv4vvvgiKSkpDZaboih88sknfPXVVxQXF3PuuecyduxYYmJiap2bX7jer/rIqaHfr7Vr17Js2TJsNhtdu3Zl6tSpJCYmVhljdX788UfeeustPB4P48aN47zzzquwffXq1cyaNavathp7TrNnz2bHjh0V9l+2bFmD5wbB/1+pSrj/vhoypxP5+6oTiqhSVlaWMmHCBGXjxo1KQUGB8uyzzyozZsxQnE6ncssttyj//e9/laysLOWJJ55Q1q1bpx6XnZ2t3HHHHcrIkSMD2tu+fbty9913K4cOHVKPe+GFF6qNo6ioSLnhhhuUDz74QDl8+LBy3333KVu3blW37927V/nXv/6l3HLLLRGRk8PhUOx2u/rYvHmzcscddygej6dBc1u7dq0yadIkZffu3cqhQ4eU6dOnKwsWLDih3BQlvO9XfeTUkO/Xn3/+qfzrX/9Sfv/9d+XIkSPKww8/rMyfP79Gr3swBw4cUMaMGaOsXbtWOXDggHL77bcrhw4dUrdv2bJFGTt2rDJr1qxq22oKOZWWlga8X2vWrFFmz57d4LkpSvDf06qE+++roXOq7d9XXZFTD9U4dOgQ119/Pf379ychIYHBgweTnp7Oli1bKCkpYfz48aSmpjJmzBjWrVunHjd37lwGDRpUob09e/Zwxhln0LJlS1JTUznvvPPIzs6uNo7vvvuOpKQkRowYQVpaGtdcc436fA6Hg3nz5jFkyJCIySk6Opq4uDj18dlnnzFy5Ei02qp/Zes6t2+//ZZhw4bRuXNnWrZsyciRI/nll19OKLdwv1/1kVNDvl9ZWVncdNNNnHbaaTRr1owLL7yQ9PT0amOsyrp16+jZsyeDBg2ibdu2XHrppXz77beA75vh4sWL6/X9auicYmJi1PfKaDTy+eefc+211zZ4bhD897Qq4f77auicavv3VVekUKhGnz59uPjii9Xlw4cPk5aWxoEDB+jatSvR0dEAtGvXjszMTHW/6dOn069fvwrttWnThp9//pmcnByKiopYt24dp512WrVxHDhwgJ49e6rdXp07d1Z/MXU6HY899hjdu3ePmJyOt2fPHnJzc+nfv3+D52a1WmnevLm6rNVqa/TH2Zjfr/rI6Xj1/X5ddNFFnHPOORWOCSXGynI79dRT1eXOnTuzb98+AEwmE08++SStW7eutp2mktPxfvrpJxITE+nWrVuD5wbBf0+ryy2cf18NndPxQvn7qityjUII3G43n376KcOGDSM7O5vk5GR1m0ajQavVYrfbMZlMpKSkkJubW6GNM844g88//5zbb78dgE6dOnHVVVcBcP/995OTk1PhmNtuu42SkpKA/6iMRiP5+fmA7w8jKSmJrKysiMnpeF988QWDBw8OuXqui9w6dOjApk2bOPfccwFYv349vXr1OqHcwv1+1UdOx2uI98vPbrezdu1a7rjjDoAqY5w4cSIej6fC886ePZuSkpKAc71Go5GCggKAgOcLVWPN6XhffPEFQ4cODUtuQNDf08b+99WQOR2vtn9fJ0IKhRAsW7aM6OhoBg4cyPvvv19hWE6DwYDT6ayyjR9//JEjR47w3HPPYbFYeOedd1iwYAHTpk3j/vvvx+12VzgmPj6e7777Dp3u2NtVk+eKhJzsdjubNm1iwoQJYcntuuuu44knnmDmzJmUlpaSkZHBnDlzAJrs+1WfOTX0+/Xqq6/StWtXzjjjDACioqKCxvjUU09VOotfUlISUVFRAc+n1+spKysLOYemltPBgwfJzs7m7LPPDktuVWkqf18NmdOJ/H2dCCkUamjbtm2sXr2axx9/HJ1Oh8lk4uDBgwH7lJaWBrzRlfnuu+8YPHiwWjlOmDCBCRMmUFxcTFJSUtDjTCYTVqs1pOeqTlPI6aeffqJ79+4hf6urq9yaN2/Os88+y6FDh3jnnXdISEhQuzSb6vtVnzk15Pu1fv16tm/fzjPPPFOjGI//dlhdbg6HIyzvV0PntHHjRs4555yQv53WVW5VaQp/Xw2dU23/vk6UXKNQA7m5ucyfP5+JEyeqH4adO3dm165dAfu4XK5q30BFUSgqKlKXCwsLAfB6vVUe16lTJ3bv3q0up6enV/lLV52mktMPP/wQcC6wJuoyN/B1OcbGxrJ161auv/76GsXQmN8vqL+cGur92rt3L6+//jpTp04lISEhpBiD5Xb884Xj/QpHTj/88AN9+/YNW2611Rj+vsKRU23+vuqCFArVcDqdPPXUU5x11lmcc845OBwOHA4Hp5xyCqWlpXz99dcAfPTRR/Tq1avayvyUU07hq6++Ys2aNaxfv57nn3+ebt26YTabqzzurLPOYufOnfzxxx+43W4++eQTevfuHdE5OZ1OduzYQc+ePcOWm9+HH37IueeeS4cOHWq0f2N+v+orp4Z6v4qKipg7dy5XXHEFnTp1Uo+pSYzB9O3bl40bN5KRkYHD4WDVqlUN+n6FI6ecnBzy8vLo2rVr2HKrrXD/fYUjp9r8fdUVOfVQjd9//53MzEwyMzP56quv1PUvvvgikydPZv78+bzzzjtoNBpmz55dbXuXXXYZR48e5cMPP1QH6rjllluqPc5isTB+/HiefPJJ9dammhzXlHP666+/iIuLo0WLFmHLDXy3x23YsIHnnnuuxnE05vcL6ienhnq/Nm7cSGFhIUuXLmXp0qXqMcuWLav1696+fXuGDh3K9OnT0ev1pKWl1fj2uqaa0/bt2+nQoQMGgyFsudVWuP++wpFTbf6+6opMM32CCgsL2bdvH126dKn2G3RdyM3N5dChQ3Tv3r1GI+nVRiTm5BeJuUViTn61za22MWZmZpKfn0+PHj1O+Jx3MJGYk18k/i5GYk6hkkJBCCGEEEHJNQpCCCGECEoKBSGEEEIEJYWCEEIIIYKSQkEIIYQQQUmhIIQQQoigpFAQQgghRFBSKAghhBAiKCkUhBBCCBGUFApCCCGECOr/AVvonQwVY7S/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(psale.index,psale.sale,label=\"原始数据\",color='r')\n",
    "plt.plot(pd.DatetimeIndex(primitiveSale.index),primitiveSale.sale,label=\"阉割数据\",color='g')\n",
    "plt.plot(forecast.index,forecast.values,label=\"预测结果\",color='b')\n",
    "plt.legend(loc=\"upper right\")\n",
    "plt.title(series+\"汽车销量预测\") # 图形标题\n",
    "plt.savefig(\"./img/\"+seriesId+'af_sale.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "assumed-method",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \"\"\"\n",
      "d:\\miniconda\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:353: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
      "  FutureWarning,\n",
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid=model.resid\n",
    "plt.rcParams['savefig.dpi'] = 100 #图片像素\n",
    "plt.rcParams['figure.dpi'] = 100\n",
    "#自相关图\n",
    "plot_acf(resid,lags=g_datalen-1-d).show()\n",
    "\n",
    "#解读：有短期相关性，但趋向于零。\n",
    "\n",
    "#偏自相关图\n",
    "plot_pacf(resid,lags=g_datalen/2-1-d).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "apart-generator",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\miniconda\\lib\\site-packages\\ipykernel_launcher.py:1: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "qqplot(resid, line='q', fit=True).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "neural-rebate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D-W检验的结果为： 1.6867016775164492\n"
     ]
    }
   ],
   "source": [
    "print('D-W检验的结果为：',sm.stats.durbin_watson(resid.values))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "twenty-headquarters",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "残差序列的白噪声检验结果为：     lb_stat  lb_pvalue\n",
      "1  0.000757   0.978049\n"
     ]
    }
   ],
   "source": [
    "# 方法一\n",
    "print('残差序列的白噪声检验结果为：',acorr_ljungbox(resid,lags=1))#返回统计量、P值"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
